code: def evaluate(self, epoch, split="val"):
      self.model.eval()
      pred_collect = []
      gt_collect = []
      assert split in ["val", "test"]
      dataloader = (
          self.val_dataloader
          if split == "val"
          else self.test_dataloader
      )
      for data in dataloader:
          graph, gt_str = data["graph_data"], data["output_str"]
          graph = graph.to(self.device)
  
          oov_dict = None
          ref_dict = self.vocab.out_word_vocab
  
          prob, _, _ = self.model(graph, oov_dict=oov_dict)
          pred = prob.argmax(dim=-1)
  
          pred_str = wordid2str(pred.detach().cpu(), ref_dict)
          pred_collect.extend(self.remove_bpe(pred_str))
          gt_collect.extend(self.remove_bpe(gt_str))
  
      score = self.metrics["BLEU"].calculate_scores(
          ground_truth=gt_collect, predict=pred_collect
      )
      self.logger.info(
          "Evaluation results in `{}` split: BLEU-1:{:.4f}\tBLEU-2:{:.4f}\tBLEU-3:{:.4f}\t"
          "BLEU-4:{:.4f}".format(
              split, score[0][0], score[0][1], score[0][2], score[0][3]
          )
      )
      self.writer.add_scalar(
          split + "/BLEU@1", score[0][0] * 100, global_step=epoch
      )
      self.writer.add_scalar(
          split + "/BLEU@2", score[0][1] * 100, global_step=epoch
      )
      self.writer.add_scalar(
          split + "/BLEU@3", score[0][2] * 100, global_step=epoch
      )
      self.writer.add_scalar(
          split + "/BLEU@4", score[0][3] * 100, global_step=epoch
      )
      return score[0][-1]
  
imports: import os
  import resource
  import numpy
  import torch
  import torch.optim
  from torch.utils.data import DataLoader
  from torch.utils.tensorboard import SummaryWriter
  from graph4nlp.pytorch.models.graph2seq import Graph2Seq
  from graph4nlp.pytorch.models.graph2seq_loss import Graph2SeqLoss
  from graph4nlp.pytorch.modules.evaluation import BLEU
  from args import get_args
  from build_model import get_model
  from dataset import IWSLT14Dataset
  from utils import WarmupCosineSchedule, get_log, wordid2str
label_desc: Use image node_id 14 for calling the NMT obj's underlying member method code with example usage: obj.evaluate(epoch, split) and returns: score